• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2005.05a
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• pp.342-345
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• 2005

This paper describes the electron energy distribution function characteristics in $SF_6-He$ gas calculated for range of E/N values from $50{\sim}700[Td]$ by the Monte Carlo simulation(MCS) and Boltzmann equation(BE) method using a set of electron collision cross sections determined by the authors and the values of electron swarm parameters are obtained by time of flight(TOF) method. The results gained that the values of the electron swarm parameters such as the electron drift velocity, the electron ionization or attachment coefficients, longitudinal and transverse diffusion coefficients agree with the experimental and theoretical for a range of E/N. The results of Boltzmann equation and Monte carlo simulation have been compared with experimental data by Pollock, Ohmori, cottrell and Walker.

• International Journal of Aeronautical and Space Sciences
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• v.7 no.1
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• pp.129-136
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• 2006

A new guidance synthesis for anti-ship missiles to control impact angle and impact time is proposed in this paper. The flight vehicle is assumed as a 1st order lag system to consider more practical system. The proposed guidance synthesis enhances the survivability of anti-ship missiles because multiple anti-ship missiles with the proposed synthesis can hit the target simultaneously. The control input to satisfy constraints of zero miss distance and impact angle, and the feedforward bias control input to control impact time constitute the guidance law. The former is from trajectory shaping guidance, the latter is from neural network. And particle swarm optimization method is introduced to furnish reference input and output for learning in neural network. The performance of the proposed synthesis in the accuracy of impact time and angle is validated by numerical examples.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.595-609
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• 2020

In this study, we propose a decentralized mathematical model for predictive control of a system of multi-autonomous unmanned aerial vehicles (UAVs), also known as drones. Being decentralized and autonomous implies that all members make their own decisions and fly depending on the dynamic information received from other unmanned aircraft in the area. We consider a variety of realistic characteristics, including time delay and communication locality. For this flocking flight, we do not possess control for central data processing or control over each UAV, as each UAV runs its collision avoidance algorithm by itself. The main contribution of this work is a mathematical model for stable group flight even in adverse weather conditions (e.g., heavy wind, rain, etc.) by adding Gaussian noise. Two of our proposed variance control algorithms are presented in this work. One is based on a simple biological imitation from statistical physical modeling, which mimics animal group behavior; the other is an algorithm for cooperatively tracking an object, which aligns the velocities of neighboring agents corresponding to each other. We demonstrate the stability of the control algorithm and its applicability in autonomous multi-drone systems using numerical simulations.

• Journal of Advanced Navigation Technology
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• v.21 no.6
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• pp.521-528
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• 2017

Research on AI is essential to build a system with collective intelligence that allows a large number of UAVs to maintain their flight while carrying out various missions. A typical approach of AI includes 'top-down' approach, which is a rule-based logic reasoning method including expert system, and 'bottom-up approach' in which overall behavior is determined through partial interaction between simple objects such as artificial neural network and Flocking Algorithm. In the same study as the existing Flocking Algorithm, individuals can not perform individual tasks. In addition, studies such as UAV formation flight can not flexibly cope with problems caused by partial flight defects. In this paper, we propose organic integration between top - down approach and bottom - up approach through multi - agent system, and suggest a flight flight algorithm which can perform flexible mission through it.

• Journal of Advanced Navigation Technology
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• v.24 no.5
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• pp.382-392
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• 2020

The use of unmanned aerial vehicle (UAV) have been regarded as a promising technique in both military and civilian applications. Nevertheless, due to the lack of relevant and regulations and laws, the misuse of illegal drones poses a serious threat to social security. In this paper, aiming at deriving the three-dimension optimal surveillance trajectories for group monitoring drones, we develop a group trajectory planner based on the particle swarm optimization and updating mechanism. Together, to evaluate the trajectories generated by proposed trajectory planner, we propose a group-objectives fitness function in accordance with energy consumption, flight risk. The simulation results validate that the group trajectories generated by proposed trajectory planner can preferentially visit important areas while obtaining low energy consumption and minimum flying risk value in various practical situations.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.205-206
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• 2021

본 논문에서는 ROS를 이용한 드론 군집 비행 시뮬레이션을 구현한 결과를 보인다. ROS 환경에서 Gazebo 시뮬레이션 툴과 ArduPilot을 이용하여 모델링된 드론을 Gazebo에 적용한 뒤, 프로그래밍된 명령을 적용하여 각각의 드론이 명령에 따라 제어되는 군집비행을 보인다. 시뮬레이션은 12대의 드론이 각각 cpp 파일에 따라 제어되도록 설정한 launch 파일을 roslaunch하여 설정한 모든 드론이 Gazebo에서 각각 제어되는 군집비행 시뮬레이션을 구현하였다.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.54 no.1
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• pp.29-33
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• 2005

Energy distribution function for electrons in $SF_6$-Ar mixtures gas by MCS-BEq algorithm has been analysed over the E/N range $30{\sim}300$[Td] by a two term Boltana equation and by a Monte Carlo Simulation using a set of electron cross sections determined by other, authors, experimentally the electron swarm parameters for 0.2[%} and 0.5[%] $SF_6$-Ar mixtures were measured by time-of-flight(TOF) method. The result show that the deduced electron drift velocities, the electron ionization or attachment coefficients, longitudinal and transverse diffusion coefficients and mean energy agree reasonably well with theoretical for a rang of E/N values. The results obtained from Booltemann equation method and Monte Carlo simulation have been compared with present and previously obtained data and respective set of electron collision cross sections of the molecules.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1998.06a
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• pp.279-283
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• 1998

The electron swarm parameters in the$CF_4$(O.l%, 5%)+Ar mixtures are measured by time of flight method over the E/N(Td) range from 10 to 300LTdl. A two-term approximation of the Boltzmann equation analysis and Monte Carlo simulation have been also used to study electron transport coefficients. We have calculated W, NDL, NDT, $\alpha$ and the limiting breakdown electric field to gas mixtures ratio in pure $CF_4$ gas and$CF_4+Ar$ mixtures. The measured results and the calculated results have been compared each other paper.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.5
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• pp.448-454
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• 2008

This paper deals with a behavior based decentralized control strategy for UAV swarming utilizing the artificial potential functions and the sliding mode control technique. Individual interactions for swarming behavior are modeled using the artificial potential functions. The motion of individual UAV is directed toward the negative gradient of the combined potential. For tracking the reference trajectory of UAV swarming, a swarming center is considered as the object of control. The sliding-mode control technique is adopted to make the proposed swarm control strategy robust with respect to the system uncertainties and the varying mission environment. Numerical simulation is performed to verify the performance of the proposed controller.

• Journal of Computing Science and Engineering
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• v.11 no.3
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• pp.79-91
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• 2017

In this paper, the abdominal-deformation measurement scheme is conducted on a shape-flexible mannequin using the DIC technique in a stereo-vision system. Firstly, during the integer-pixel displacement search, a novel fractal dimension based on an adaptive-ellipse subset area is developed to track an integer pixel between the reference and deformed images. Secondly, at the subpixel registration, a new mutual-learning adaptive particle swarm optimization (MLADPSO) algorithm is employed to locate the subpixel precisely. Dynamic adjustments of the particle flight velocities that are according to the deformation extent of each interest point are utilized for enhancing the accuracy of the subpixel registration. A test is performed on the abdominal-deformation measurement of the shape-flexible mannequin. The experiment results indicate that under the guarantee of its measurement accuracy without the cause of any loss, the time-consumption of the proposed scheme is significantly more efficient than that of the conventional method, particularly in the case of a large number of interest points.